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• W2949202375 startingPage "5785" @default.
• W2949202375 abstract "To promote the applications of semantic segmentation, quality evaluation is important to assess different algorithms and guide their development and optimization. In this paper, we establish a subjective semantic segmentation quality assessment database based on the stimulus-comparison method. Given that the database reflects the relative quality of semantic segmentation result pairs, we adopt a robust regression mapping model to explore the relationship between subjective assessment and objective distance. With the help of the regression model, we can examine whether objective metrics coincide with subjective judgement. In addition, we propose a novel relative quality prediction network (RQPN) based on Siamese CNN as a new objective metric. The metric is trained by our subjective assessment database and can be applied to evaluate the performances of semantic segmentation algorithms, even if the algorithms were not used to build the database. Experiments are conducted to show the advance and the reliability of our database and demonstrate that results predicted by RQPN are more consistent to subjective assessment than existing objective metrics." @default.
• W2949202375 created "2019-06-27" @default.
• W2949202375 creator A5006748765 @default.
• W2949202375 creator A5030789783 @default.
• W2949202375 date "2019-12-01" @default.
• W2949202375 modified "2023-09-23" @default.
• W2949202375 title "Visual Quality Evaluation for Semantic Segmentation: Subjective Assessment Database and Objective Assessment Measure" @default.
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• W2949202375 doi "https://doi.org/10.1109/tip.2019.2922072" @default.
• W2949202375 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31217113" @default.
• W2949202375 hasPublicationYear "2019" @default.
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